As the society develops and of people's life improves, the demands for oil and gas resources are increasing. Thus, it is necessary to accelerate the exploration of the oil and gas resources in complex geological structure areas. Seismic imaging is a significant part of seismic exploration, and the improvement of the computational efficiency of the migration imaging technologies will be of great significance to the oil-gas exploration.
Gaussian beam migration has been widely applied to the migration imaging. The Gaussian beam migration has advantages of Kirchhoff migration, such as efficiency and steep dip imaging, and it also makes it easy to deal with problems such as caustics and multi-valued travel time. It is proved by practice that the Gaussian beam migration is an excellent migration technology. Hill has given specific implementation of representing plane waves by Gaussian beams and of the Gaussian beam migration, which lays a foundation for the Gaussian beam migration. Thereafter, scientific researchers did a lot of extending studies on the Gaussian beam migration, such as anisotropic Gaussian beam migration, true-amplitude Gaussian beam migration, Gaussian beam reverse migration, dynamically focused Gaussian beam migration and sparse Gaussian beam migration.
The Gaussian beam migration includes three steps: decomposing seismic data and representing the seismic data with Gaussian beams; propagating the Gaussian beams downward; and superposing, according to an imaging condition, contributions of the Gaussian beams at an imaging point. The decomposing the seismic data and representing the seismic data with Gaussian beams is the key factor for the Gaussian beam migration, which decides the computation amount and imaging results of the migration.
In the existing sparse Gaussian beam migration imaging methods, sparse decomposition is applied to the seismic data by using Gaussian beams with a curvature of zero. But the seismic data has a curvature, and therefore, both a width of a Gaussian beam based function and a spacing between centers of two adjacent Gaussian beams should be small enough to enable appropriate fitting of the seismic data. However, in the existing Gaussian beam migration imaging methods, a large number of waveform functions are obtained through decomposition, and it is required to perform migration imaging on each of the waveform functions during the migration of seismic imaging, and thus the computational efficiency of the entire migration is low.